Unsupervised learning

In this case, you only have input data (x) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about it.

In unsupervised learning, you may not have any data in the beginning. Say for example on the same scenario discussed above in supervised learning, you have a basket full of fruits and you are asked to group them into similar groups. But you don't have any previous data or there are no training or labeling is done earlier. In that case, you need to understand the domain first because you have no idea whether the input is a fruit or not. In that case, you need to first understand all the characteristics of every input and then to try to match with every new input. May be at the final step you might have classified all the red color fruits into one baskets and the green color fruits into another basket. But not an accurate classification. This is called as unsupervised learning.